Our main contributions can be summarised as:
1) we solve the policy learning bottleneck, which requires to have an offline training stage, with the first online policy learning by using the POMCP technique;
2) we evaluate our approach according to Active Vision Dataset (AVD) benchmark [1]}, and show that it outperforms alternative approaches in terms of success rate without the cost of offline training under certain cases, and
3) we perform an ablation study to assess the behaviour of the proposed approach when fed with increasingly corrupted detections and prove the robustness of our approach against missing detections.
